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2024 IEEE International Conference on Computer Vision and Machine Intelligence, CVMI 2024

作者： Srinidhi, Srivageesh K Vishal, K.S. Shashank, U Someswara Nidhin Prabhakar, T.V. Singh, Rimjhim Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376876

In the rapidly evolving field of information security, video steganography offers a promising solution for concealing sensitive data within multimedia content. The proposed approach introduces a novel approach that integrates video compression, audio embedding, and encryption to enhance security and efficiency in information transmission. The method follows a three-stage process, H.264 video compression to reduce file size, audio embedding using Least Significant Bit (LSB) steganography to conceal data within video frames, and Galois/Counter Mode (GCM) encryption to ensure robust cryptographic protection. Key findings demonstrate that H.264 compression effectively reduces video size while maintaining quality, LSB embedding conceals audio with minimal visual impact, and GCM encryption provides robust protection against unauthorized access and tampering. This comprehensive method offers a viable solution for secure, covert information transmission in multimedia, addressing critical needs in modern information security by combining steganography, compression, and encryption for a more robust and efficient approach than traditional methods. © 2024 IEEE.

关键词： Video recording

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Dissecting Digital Dangers: A MITRE-Aligned Analysis of Two Prominent Malware Instances 15

Dissecting Digital Dangers: A MITRE-Aligned Analysis of Two ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rao, Subhiksha Suresh Mishra, Sahil Rajesh, M. Savant, Richa Vivek Sreevidya, B. Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350370249

The increasing complexity of malware requires a more thorough comprehension of how they operate. Using the MITRE ATT&CK framework, this project delves into the TrickBot and AsyncRAT malwares, offering a thorough examination of their tactics, techniques, and procedures (TTPs) across the cyber kill chain. This study uncovers trends in how these malicious software gain entry, carry out tasks, remain active, and extract information from a network by correlating TTPs with ATT&CK methods. Key analyses indicate that TrickBot utilizes elevated entropy values to avoid being detected, incorporates modular structures to enhance its capabilities, and utilizes spear-phishing and network spreading to achieve broad infection. AsyncRAT is found to employ sophisticated anti-analysis methods, reflective code loading, and achieves persistence by altering the system registry while avoiding sandbox environments. Additionally, it establishes command and control (C2) connections to maintain long-term access over compromised systems. The observations uncovered in this research emphasize the importance of having strong cybersecurity policies and comprehensive threat intelligence to combat changing threats effectively. Understanding the complex behaviours and evasion methods of these malwares is crucial for creating specific defences and improving security overall. ©2024 IEEE.

关键词： Phishing

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Infield disease detection in citrus plants: integrating semantic segmentation and dynamic deep learning object detection model for enhanced agricultural yield

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Neural computing and Applications 2024年第35期36卷 22485-22510页

作者： Rani, N. Shobha Krishna, Arun Sri Sunag, M. Sangamesha, M.A. Pushpa, B.R. University Bengaluru India Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru India Department of Chemistry The National Institute of Engineering Mysuru India

Simultaneous and dynamic detection of diseases from infield images of citrus plants is the primary objective of this investigation. Refraining from discarding the disease-related issues would reduce agricultural yield and production, resulting in significant crop loss and economic instability. Computer Vision offers an inexpensive and efficient solution for detecting and predicting multiple diseases from infield images of citrus plants in various stages of plant growth. This study aims to perform simultaneous detection and prediction of cankers, mites, and nutritional deficiencies from infield images of citrus plants. For this purpose, over 4914 samples are annotated that are obtained from 441 image samples collected from orchards of citrus plants. Initially, the input image is subject to preprocessing followed by segmentation of prominent leaf regions and disease prediction. Contributions investigated in this work are two-fold: a deep semantic segmentation model named Dynamic U-Net is employed to extract prominent regions of interest, and a dynamic, lightweight object detection deep learning model for predicting diseases. From experimental outcomes, the segmentation efficiency is found to be 89.07% foreground accuracy, 0.7881 of IoU, and 0.9188 of the Dice coefficient. The object detection performance is quantified using the mapped metric, resulting in 0.85, 0.71, 0.64, and 0.27 efficiency concerning plant diseases, cankers, mites, and nutritional deficiency. As per the findings, the proposed approach is an effective solution to perform automated detection and prediction from infield images of citrus plants. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Citrus fruits

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Empowering Linguistic Diversity: Bridging Cultures through Neural Machine Translation from Dzongkha to Hindi 15

Empowering Linguistic Diversity: Bridging Cultures through N...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Ganguly, Tania Santhanalakshmi, S. Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

Bhutan and India have enjoyed longstanding friendly relations, fostering significant travel between the two nations. However, linguistic differences often impede visitors' comprehension of cultural beliefs, particularly between Dzongkha, Bhutan's native language, and Hindi, widely spoken in India. This paper presents an innovative language translation solution leveraging Neural Machine Translation (NMT) models which is the first and only solution to overcome these barriers. The goal is to enrich tourists' experiences by providing comprehensive insights into the cultural intricacies of both countries. Through extensive testing, the proposed methodology achieves a promising BLEU score of 0.416, offering an effective means to bridge the linguistic gap and foster cross-cultural understanding. © 2024 IEEE.

关键词： Long Short Term Memory (LSTM) Low Resource Language (LRL) Neural Machine Translation (NMT) Sentence Length Analysis (SLA)

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Unraveling IoT Network Security with Snort for Robust Intrusion Detection and Prevention 2

Unraveling IoT Network Security with Snort for Robust Intrus...

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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Geetha, K. Sreedevi, A.G. Chadha, Angela Raj Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383652

The Internet of Things (IoT) has brought unprecedented connectivity, transforming technology across industries. This interconnected landscape, however, presents unprecedented network security challenges. This paper examines Snort's role as a powerful tool for Intrusion Detection in the network and the ways to prevent the intrusion detection and thoroughly investigates the security challenges posed by IoT networks. In light of the growing number of connected devices, the study focuses on specific vulnerabilities, emphasizing issues such as resource constraints, diverse communication protocols, and the need for tailored security measures. The paper provides a thorough examination of Snort's architecture and its application through the development of custom rules for detecting IoT threats. Practical implementation strategies such as network segmentation and continuous monitoring are investigated. The conclusion emphasizes how critical it is to understand threats unique to the Internet of Things and how to use Snort to fortify network defenses in this complex and ever-changing ecosystem. © 2024 IEEE.

关键词： Network intrusion

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Design and Development of Visual Error Detection System (VEDS) Leveraging Convolutional Neural Networks for Early Detection of Ocular Abnormalities to Mitigate Vision-Related Complications

Design and Development of Visual Error Detection System (VED...

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2024 International Conference on Computational Intelligence and computing Applications, ICCICA 2024

作者： Krishna, Kaivaram Sivarama Deepak Kumar, Bommala Reddy, Morumpalli Deekshith Saketh, Ch. Sreevidya, B. Rajesh, M. Amrita School Of Computing Department Of Computer Science And Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350306446

Ocular disorders are one of the severe eye disease The proposed system is to design and develop an automated system which captures the patient's eye image and uses the benefits of Convolutional Neural Networks (CNN) to predict whether the patient suffers from ocular eye disorders or not. The functionality integrates MongoDB database administration with a visual error detection system. The model is employed in a web-based platform that enhances the usefulness of MongoDB overall by utilizing convolutional neural networks (CNN) to interface with it smoothly. The CNN was selected based on its accuracy and trained on several datasets that included around 5000 photos for each category of eye illness from reputable databases including Eye Recognition, IDRiD, and HRF. Instant eye deployment is possible with the user-friendly website while retinal pictures load. Integration with MongoDB enhances user experience, saves and retrieves diagnostic data, and manages data better. Numerous outcomes supported the model's efficacy and indicated a significant advancement in the early diagnosis and treatment of ocular disorders. © 2024 IEEE.

关键词： Convolutional neural networks

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Interactome: A Platform for Comprehensive Drug-Drug Interaction Analysis 15

Interactome: A Platform for Comprehensive Drug-Drug Interact...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sivakumar, Amrutha Bhanot, Shreya Srinidhi, M. Aiswariya Milan, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

Drug-drug interaction is an important aspect to be taken into consideration when administering multiple drugs to patients with severe diseases. It is important to know if the combination of the drugs causes a positive effect or an ill effect on the person's body. Thus, Interactome roots to offer a comprehensive solution to assess and find out the interactions between commonly used drugs for the treatment of different ailments such as heart diseases or diabetes and provides an understanding of how the combination of these drugs can cause side effects if not provided with enough details about the drug. Thus, it enables the user to know alternatives to using efficient drugs without any side effects by displaying graphs of the percentage of positive and negative effects and charts depicting the effects. By providing negative and positive feedback on the combination of various drugs used for heart and diabetes patients and displaying the percentage risks with the help of graphs and charts, Interactome ensures the prevention of any unnecessary attacks. Also, it saves the lives of an individual by spreading awareness about drug-drug interactions. We incorporate a Trie data structure that efficiently analyzes the input medications against a database of known interactions. It optimizes the retrieval process by organizing the drugs into a hierarchical structure. Then it ensures rapid and accurate results so that healthcare professionals and individuals receive timely and comprehensive insights into potential drug interactions. © 2024 IEEE.

关键词： charts Drug-drug interaction graphs negative effects positive effects Trie data structure

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Secure Authentication Scheme for Internet of Battlefield Things 15

Secure Authentication Scheme for Internet of Battlefield Thi...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Ashutosh, Devu Pawan Akhtar, Shoaib Yeshwanth Reddy, G. Singh, Hardik K. Nimmy, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Future military operations will increasingly depend on interconnected technology, using advanced embedded systems and AI to improve defense capabilities and technology. The Internet of Battlefield Things (IoBT) will link soldiers with smart battlefield equipment such as armor, radios, and weapons, reducing decision cycle times, improving tactical analytics, and delivering precise AI support where needed. Authentication plays an important role in IoBT, as confidential information generated from battlefield devices needs to be secured from enemies. We propose a novel authentication approach designed to address the particular constraints of the IoBT, utilizing fuzzy extractor and geometric secret sharing techniques. This approach overcomes the weaknesses caused by dynamic and hostile combat situations, in which standard authentication methods may fail. The proposed scheme is based on fingerprint biometrics and fuzzy extractors, enabling reliable authentication even when dealing with noisy biometric data. Moreover, geometric secret key sharing improves security by providing secure key establishment. Also, the suggested methodology is ideal for resource-sparse Internet of Things (IoT) devices which are commonly found in military situations. The effectiveness and robustness of the proposed authentication design are shown by exhaustive analysis employing Scyther tool and simulations, illustrating its ability to resist numerous security attacks prevalent in IoBT environments. © 2024 IEEE.

关键词： Authentication

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A Multi-Class Classification Framework with SMOTE Based Data Augmentation Technique for Alzheimer's Disease Progression

A Multi-Class Classification Framework with SMOTE Based Data...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Krishna, Kaivaram Sivarama Kumar, Bommala Deepak Reddy, Morumpalli Deekshith Saketh, C.H. Ramasamy, Gayatri Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350371567

The detection of Alzheimer's disease is a critical task in medical diagnostics due to its rapid progression and profound impact on cognitive function. Deep learning (DL) offers unprecedented capabilities in analyzing medical imaging data, particularly in neuroimaging like MRI scans. However, class imbalances within the dataset persist, making it difficult to discern intricate patterns indicative of Alzheimer's pathology. This project aims to overcome this challenge by integrating advanced DL techniques with data augmentation methodologies, specifically SMOTE. This strategic integration aims to overcome the limitations of class imbalances within the initial MRI dataset, enhancing the precision and reliability of the classification model. This interdisciplinary exploration aims to redefine Alzheimer's detection capabilities, enhancing diagnoses and offering the potential for broader applications in understanding and managing complex neurodegenerative disorders. © 2024 IEEE.

关键词： Classification (of information)

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Forecasting India's Air Quality: A Machine Learning Approach for Comprehensive Analysis and Prediction 4

Forecasting India's Air Quality: A Machine Learning Approach...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Kothari, Osho Sah, Nabin Kumar Kumar, Konda V S Harshith Nair, Priyanka C Sampath, Nalini Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

With rising industrialization, India confronts increasing difficulties in maintaining air quality regulations. This research proposes a comprehensive analysis and prediction framework based on machine learning approaches for assessing and forecasting the Air Quality Index (AQI) in different place of India. This model takes into account a wide variety of pollutant concentrations that are monitored at regular intervals. This approach, which uses historical data from numerous Indian cities, employs a variety of machine learning methods, including k-Neighbour regression, decision tree regressor, linear regression, and random forest regressor, in addition to support vector regressor. Every model had performed well but the Decision Tree Regression and Random forest has performed best achieving RMSE value as 0.0016 and 0.00095 respectivelly whereas R-Squared value as 0.99 for both the model, indicating strong performance and a substantial progress in air quality prediction. The environmental health risks associated with air pollution have been mitigated by the addition of important knowledge. © 2024 IEEE.

关键词： Support vector regression

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